The Tradeoffs Between Open and Traditional Relation Extraction

Traditional Information Extraction (IE) takes a relation name and hand-tagged examples of that relation as input. Open IE is a relation- independent extraction paradigm that is tai- lored to massive and heterogeneous corpora such as the Web. An Open IE system extracts a diverse set of relational tuples from text with- out any relation-specific input. How is Open IE possible? We analyze a sample of English sentences to demonstrate that numerous rela- tionships are expressed using a compact set of relation-independent lexico-syntactic pat- terns, which can be learned by an Open IE sys- tem. What are the tradeoffs between Open IE and traditional IE? We consider this question in the context of two tasks. First, when the number of relations is massive, and the rela- tions themselves are not pre-specified, we ar- gue that Open IE is necessary. We then present a new model for Open IE called O-CRF and show that it achieves increased precision and nearly double the recall than the model em- ployed by TEXTRUNNER, the previous state- of-the-art Open IE system. Second, when the number of target relations is small, and their names are known in advance, we show that O-CRF is able to match the precision of a tra- ditional extraction system, though at substan- tially lower recall. Finally, we show how to combine the two types of systems into a hy- brid that achieves higher precision than a tra- ditional extractor, with comparable recall.